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Published： 16 March 2021 ，
Accepted： 04 May 2020
移动端阅览
Zhiguang Zhou, Wanying Xie, Weihua Zheng, Yuanyuan Chen. Visual analytics of spatial and temporal correlation features of rainfall. [J]. Journal of Image and Graphics 26(3):619-632(2021)
目的
2
降水是影响全球气候变化和系统环境的重要因素，面向降水数据开展时空关联分析，对于区域气候特征探索及异常情况监测具有重要的意义。然而，降水时空关联特征的分析是一个复杂且耗时的过程，与气象站点的空间分布以及降水的时间序列密切相关。本文综合考虑降水的时空变化特征，研究和设计面向降水数据时空关联特征分析的可视化系统工具。
方法
2
利用地图和矩阵图呈现降水数据的空间分布和周期变化特征，设计径向盒须图对降水数据的时空变化异常特征进行捕获；通过局部Moran's I指数的计算和热力图的呈现表达降水的空间相关性，支持用户交互式地探索空间相关性的时序变化特征；利用普通克里金插值模型获得降水空间插值图，并对插值结果的准确性进行可视化评估。
结果
2
以中国安徽省1971-2014年气象观测站长时间序列月降水数据集为例进行分析，实验结果证明本研究可视化交互系统能够直观高效地探索区域降水长时间序列时空变化特征和极端降水情况；有效探究区域降水空间分布模式、不同站点降水信息间空间依赖性和异质性，并快速发现降水奇异点；分析区域不同时间尺度降水气候特征空间变化。
结论
2
系统工具集成便捷的交互模式，支持用户探索式地分析降水数据的时空关联特征，进而有效地探究区域气候变化规律和特征分布关系。基于真实降水数据的实验结果以及降水领域专家的反馈，进一步验证了本文系统工具的有效性和实用性。
Objective
2
Rainfall is one of the important factors that affects global climate change and system environment. Conducting the spatiotemporal correlation analysis of rainfall data for exploring regional climate characteristics and monitoring climate abnormal conditions has great significance. However
the spatial and temporal correlation analysis of rainfall data is a complicated
time-consuming process due to the drastic spatiotemporal variation and spatial heterogeneity of rainfall
and the diversity of rainfall data. It is closely related to the geographical locations of meteorological stations and the time series of rainfall. Therefore
a visual analysis method for the spatiotemporal correlation analysis of rainfall data in the paper is proposed by considering the spatial locations of meteorological stations and the rainfall variations over a long period of time. Based on spatiotemporal visualization analysis of rainfall data
the spatiotemporal correlation characteristics and climate characteristics of rainfall are further quantified.
Method
2
A map view is first used to reflect the basic information of rainfall and meteorological stations
enabling users to select meteorological stations interactively. The spatial correlation of rainfall data is explored by using the local Moran index
allowing the interactive selection of one or more sites and the temporal analysis of the mean value of the local Moran index. The temporal changes and outliers of rainfall data are presented by using periodic matrix graph and the circular box-and-whisker graph
respectively. The spatial interpolation map is obtained by an ordinary Kriging interpolation method
and the accuracy of the interpolation result is further evaluated. Finally
a set of continent user interactions is integrated into the visualization system to help users easily conduct spatiotemporal exploration of rainfall data and deeply obtain insights into the features of interest hidden in the complex rainfall data structures.
Result
2
Taking the monthly rainfall data set of Anhui meteorological observation stations from 1971 to 2014 as an example
the experimental results show the following. 1) The visual interactive system of our study can intuitively and efficiently explore the spatiotemporal variation characteristics and anomalies of regional rainfall time series. The spatial distribution differences of regional precipitation can be clearly shown in the map view. The periodic matrix view can directly reflect the basic periodic rule that the rainfall is mainly concentrated in the spring and summer seasons and the minimum in winter
and reflect the interannual differences of rainfall in different months of the station and several evident rainfall anomalies. Furthermore
the long-term series variability of precipitation in different months and the drought and flood conditions in different seasons from 1971 to 2010 in Anhui Province can be rapidly analyzed and observed by the box-and-whisker graph and ring graph. 2) The spatial distribution pattern
spatial dependence
and heterogeneity of rainfall information of different stations can be effectively explored by using the thermal diagram and time series diagram of Moran's I values. Results show that the monthly rainfall in Anhui Province has a strong local aggregation pattern and zonal regularity
and a different spatial structure at different times
which reflects the complex
changeable influence of climate and terrain on rainfall. 3) The spatial variation of regional rainfall climate characteristics at different time scales can be clearly analyzed by using the precipitation spatial interpolation map using ordinary Kriging method. Unexpectedly
the average precipitation in July of Anhui Province from 1971 to 2010 is mostly concentrated in the north
west
and southwest.
Conclusion
2
A visual analysis system is developed to help users explore and analyze the spatiotemporal correlation
extreme conditions
and regional climate features of rainfall data interactively. It is more intuitive
efficient
and easier to operate than the traditional analysis software. The system can effectively discover several unexpected phenomena and conclusions of experts
and further verify the existing conclusions. Furthermore
the system has good extensibility
which can be applied to the visual analysis of not only spatiotemporal correlation characteristics of precipitation data but also other geographical spatiotemporal observation data
such as temperature data
sunshine hour data
and air quality data. Our visual analysis system can help experts in related fields explore and analyze rainfall spatiotemporal data. A large amount of experimental results and expert feedback further verify the effectiveness and practicality of the proposed method. Only precipitation data are analyzed
a large amount of other types of meteorological monitoring data remain
and substantial spatial and temporal characteristics between the data are noted. The function of visual analysis in this paper must be further expanded to achieve the visual analysis of multiple meteorological monitoring data. In addition
a certain correlation is observed between different meteorological monitoring data. Although this system can effectively explore the spatiotemporal correlation characteristics of rainfall data
the correlation characteristics and modeling between rainfall data and other meteorological monitoring data have not been involved. In the future work
we will focus on the development of visual analysis methods for relationship modeling of multiple meteorological monitoring data.
降水时空关联特征周期变化特征Moran's I指数普通克里金插值
rainfallspatio-temporal correlation featuresperiodic variation characteristicMoran's Iordinary Kriging interpolation
Bai A J, Liu C H and Liu X D. 2008. Diurnal variation of summer rainfall over the Tibetan Plateau and its neighboring regions revealed by TRMM multi-satellite precipitation analysis. Chinese Journal of Geophysics, 51(3): 704-714
白爱娟, 刘长海, 刘晓东. 2008. TRMM多卫星降水分析资料揭示的青藏高原及其周边地区夏季降水日变化. 地球物理学报, 51(3): 704-714[DOI:10.3321/j.issn:0001-5733.2008.03.011]
Buchmüller J, Jäckle D, Cakmak E, Brandes U and Keim D A. 2019. MotionRugs: visualizing collective trends in space and time. IEEE Transactions on Visualization and Computer Graphics, #25(1): 76-86[DOI:10.1109/TVCG.2018.2865049]
Cao Q L, Rui G Q and Liang Y. 2018. Study on PM2.5 pollution and the mortality due to lung cancer in China based on geographic weighted regression model. BMC Public Health, 18(1): 925[DOI:10.1186/s12889-018-5844-4]
Chen S, Li S H, Chen S M and Yuan X R. 2020. R-map: a map metaphor for visualizing information reposting process in social media. IEEE Transactions on Visualization and Computer Graphics, 26(1): 1204-1214[DOI:10.1109/TVCG.2019.2934263]
Chen S M, Chen S, Lin L J, Yuan X R, Liang J and Zhang X L. 2017. E-map: a visual analytics approach for exploring significant event evolutions in social media//Proceedings of 2017 IEEE Conference on Visual Analytics Science and Technology. Phoenix: IEEE: 36-47[DOI: 10.1109/VAST.2017.8585638http://dx.doi.org/10.1109/VAST.2017.8585638]
Chen W, Zhu B and Zhang H X. 2016. BN-mapping: visualanalysis of geospatial data with Bayesian network. Chinese Journal of Computers, 39(7): 1281-1293
陈为, 朱标, 张宏鑫. 2016. BN-Mapping: 基于贝叶斯网络的地理空间数据可视分析. 计算机学报, 39(7): 1281-1293[DOI:10.11897/SP.J.1016.2016.01281]
Chuai X W and Feng J X. 2019. High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China. Science of the Total Environment, 686: 828-837[DOI:10.1016/j.scitotenv.2019.05.138]
Fang J, Yang W T, Luan Y B, Du J, Lin A W and Zhao L. 2019. Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China. Atmospheric Research, 223: 24-38[DOI:10.1016/j.atmosres.2019.03.001]
Gao H Y, Yang Q and Liang Y H. 2008. Spatialtemporal distribution of precipitation in the Aksu river basin, Xinjiang. Arid Zone Research, 25(1): 70-74
高惠芸, 杨青, 梁岩鸿. 2008. 新疆阿克苏河流域降水的时空分布. 干旱区研究, 25(1): 70-74[DOI:10.13866/j.azr.2008.01.007]
Guo D S and Zhu X. 2014. Origin-destination flow data smoothing and mapping. IEEE Transactions on Visualization and Computer Graphics, 20(12): 2043-2052[DOI:10.1109/TVCG.2014.2346271]
Hu X and Lei X S. 2009. Spatial analysis and visualization of regional precipitation in China based on ArcGIS. Science and Technology Information, (24): 115-116
胡霞, 雷星松. 2009. 基于ArcGIS的中国区域降水空间分析及其可视化. 科技资讯, (24): 115-116[DOI:10.3969/j.issn.1672-3791.2009.24.092]
Liu D Y, Weng D, Li Y H, Bao J, Zheng Y, Qu H M and Wu Y C. 2017. SmartAdp: visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics, 23(1): 1-10[DOI:10.1109/TVCG.2016.2598432]
Liu Y B, Fu Q N, Song P, Zhao X S and Dou C C. 2011. Satellite retrieval of precipitation: an overview. Advances in Earth Science, 26(11): 1162-1172
刘元波, 傅巧妮, 宋平, 赵晓松, 豆翠翠. 2011. 卫星遥感反演降水研究综述. 地球科学进展, 26(11): 1162-1172
Lu J, Zhang Y H, Gao F and Liu Y J. 2019. Temporal and spatial variation characteristics of extreme precipitation in the Sanjiang plain in recent 40 years. Research of Soil and Water Conservation, 26(2): 272-282
鲁菁, 张玉虎, 高峰, 刘玉洁. 2019. 近40年三江平原极端降水时空变化特征分析. 水土保持研究, 26(2): 272-282[DOI:10.13869/j.cnki.rswc.2019.02.039]
Mao C Y, Gong L Q, Liao J Y and Li H W. 2018. The spatial temporal features of precipitation in Zhejiang province based on satellite data. Mid-Low Latitude Mountain Meteorology, 42(2): 7-12
毛程燕, 龚理卿, 廖君钰, 李浩文. 2018. 基于卫星探测资料分析浙江省降水的时空分布特征. 中低纬山地气象, 42(2): 7-12[DOI:10.3969/j.issn.1003-6598.2018.02.002]
Michaelides S, Levizzani V, Anagnostou E, Bauer P, Kasparis T and Lane J E. 2009. Precipitation: measurement, remote sensing, climatology and modeling. Atmospheric Research, 94(4): 512-533[DOI:10.1016/j.atmosres.2009.08.017]
Packer E, Bak P, Nikkilä M, Polishchuk V and Ship H J. 2013. Visual analytics for spatial clustering: using a heuristic approach for guided exploration. IEEE Transactions on Visualization and Computer Graphics, 19(12): 2179-2188[DOI:10.1109/TVCG.2013.224]
Qu W G, Xu P P and Qian H. 2019. Analysis of precipitation characteristics and forecast of annual precipitation in Huashan region. Research of Soil and Water Conservation, 26(3): 128-134
屈文岗, 徐盼盼, 钱会. 2019. 华山地区降水特征分析与年降水量预测. 水土保持研究, 26(3): 128-134[DOI:10.13869/j.cnki.rswc.20190118.001]
Rahman S and Towfiqul Islam A R. 2019. Are precipitation concentration and intensity changing in Bangladesh overtimes? Analysis of the possible causes of changes in precipitation systems. Science of the Total Environment, 690: 370-387[DOI:10.1016/j.scitotenv.2019.06.529]
Shi Y Y, Han B T, Han L and Wei Z X. 2019. Uncovering the national and regional household carbon emissions in China using temporal and spatial decomposition analysis models. Journal of Cleaner Production, 232: 966-979[DOI:10.1016/j.jclepro.2019.05.302]
Sun G D, Liang R H, Qu H M and Wu Y C. 2017. Embedding spatio-temporal information into maps by route-zooming. IEEE Transactions on Visualization and Computer Graphics, 23(5): 1506-1519[DOI:10.1109/TVCG.2016.2535234]
Sun H W, Zhang Y R, Liao W H, Gui D W, Xue J and Yan D. 2018. A rainstorm temporal-spatial analysis method based on satellite remote sensing data. China Rural Water and Hydropower, (8): 77-85, 90
孙怀卫, 张伊然, 廖卫红, 桂东伟, 薛杰, 严冬. 2018. 基于全球卫星降水数据的暴雨时空分析方法研究. 中国农村水利水电, (8): 77-85, 90[DOI:10.3969/j.issn.1007-2284.2018.08.017]
Turkay C, Slingsby A, Hauser H, Wood J and Dykes J. 2014. Attribute signatures: dynamic visual summaries for analyzing multivariate geographical data. IEEE Transactions on Visualization and Computer Graphics, 20(12): 2033-2042[DOI:10.1109/tvcg.2014.2346265]
Wang J F, Ge Y, Li L F, Meng B, Wu J L, Bai Y C, Du S H, Liao Y L and Hu M G. 2014. Spatiotemporal data analysis in geography. Acta Geographica Sinica, 69(9): 1326-1345
王劲峰, 葛咏, 李连发, 孟斌, 武继磊, 柏延臣, 杜世宏, 廖一兰, 胡茂桂. 2014. 地理学时空数据分析方法. 地理学报, 69(9): 1326-1345[DOI:10.11821/dlxb201409007]
Wang W R. 2018. Spatio-temporal Variation of Precipitation Based on TRMM Data Downscaling in Northwestern China. Lanzhou: Lanzhou University
王万瑞. 2018. 基于TRMM数据的中国西北降水时空变化研究. 兰州: 兰州大学
Wang Y, Shen Q M, Archambault D, Zhou Z G, Zhu M, Yang S X and Qu H M. 2016. AmbiguityVis: visualization of ambiguity in graph layouts. IEEE Transactions on Visualization and Computer Graphics, 22(1): 359-368[DOI:10.1109/TVCG.2015.2467691]
Wang Z C, Ye T Z, Lu M, Yuan X R, Qu H M, Yuan J and Wu Q L. 2014. Visual exploration of sparse traffic trajectory data. IEEE Transactions on Visualization and Computer Graphics, 20(12): 1813-1822[DOI:10.1109/tvcg.2014.2346746]
Yang H L, Xiao H, Guo C W and Sun Y. 2019. Spatial-temporal analysis of precipitation variability in Qinghai Province, China. Atmospheric Research, 228: 242-260[DOI:10.1016/j.atmosres.2019.06.005]
Yuan X R, Xiao H, Guo H Q, Guo P H, Kendall W, Huang J and Zhang Y X. 2010. Scalable multi-variate analytics of seismic and satellite-based observational data. IEEE Transactions on Visualization and Computer Graphics, 16(6): 1413-1420[DOI:10.1109/TVCG.2010.192]
Zhou X, Luo Y L and Guo X L. 2015. Application of a CMORPH-AWS merged hourly gridded precipitation product in analyzing characteristics of short-duration heavy rainfall over southern China. Journal of Tropical Meteorology, 31(3): 333-344
周璇, 罗亚丽, 郭学良. 2015. CMORPH卫星-地面自动站融合降水数据在中国南方短时强降水分析中的应用. 热带气象学报, 31(3): 333-344[DOI:10.16032/j.issn.1004-4965.2015.03.005]
Zhou Z G, Meng L H, Tang C, Zhao Y, Guo Z Y, Hu M X and Chen W. 2019. Visual abstraction of large scale geospatial origin-destination movement data. IEEE Transactions on Visualization and Computer Graphics, 25(1): 43-53[DOI:10.1109/TVCG.2018.2864503]
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